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🤗 Transformers
Estado da Arte para Aprendizado de Máquina em PyTorch, TensorFlow e JAX. O 🤗 Transformers disponibiliza APIs para facilmente baixar e treinar modelos pré-treinados de última geração. O uso de modelos pré-treinados pode diminuir os seus custos de computação, a sua pegada de carbono, além de economizar o tempo necessário para se treinar um modelo do zero. Os modelos podem ser usados para diversas tarefas:
- 📝 Textos: classificação, extração de informações, perguntas e respostas, resumir, traduzir e gerar textos em mais de 100 idiomas.
- 🖼 Imagens: classificação, deteção de objetos, e segmentação.
- 🗣 Audio: reconhecimento de fala e classificação de áudio.
- 🐙 Multimodal: perguntas tabeladas e respsostas, reconhecimento ótico de charactéres, extração de informação de documentos escaneados, classificação de vídeo, perguntas e respostas visuais.
Nossa biblioteca aceita integração contínua entre três das bibliotecas mais populares de aprendizado profundo: Our library supports seamless integration between three of the most popular deep learning libraries: PyTorch, TensorFlow e JAX. Treine seu modelo em três linhas de código em um framework, e carregue-o para execução em outro.
Cada arquitetura 🤗 Transformers é definida em um módulo individual do Python, para que seja facilmente customizável para pesquisa e experimentos.
Se você estiver procurando suporte do time da Hugging Face, acesse
Conteúdo
A documentação é dividida em cinco partes:
INÍCIO contém um tour rápido de instalação e instruções para te dar um empurrão inicial com os 🤗 Transformers.
TUTORIAIS são perfeitos para começar a aprender sobre a nossa biblioteca. Essa seção irá te ajudar a desenvolver habilidades básicas necessárias para usar o 🤗 Transformers.
GUIAS PRÁTICOS irão te mostrar como alcançar um certo objetivo, como o fine-tuning de um modelo pré-treinado para modelamento de idioma, ou como criar um cabeçalho personalizado para um modelo.
GUIAS CONCEITUAIS te darão mais discussões e explicações dos conceitos fundamentais e idéias por trás dos modelos, tarefas e da filosofia de design por trás do 🤗 Transformers.
API descreve o funcionamento de cada classe e função, agrupada em:
CLASSES PRINCIPAIS para as classes que expõe as APIs importantes da biblioteca.
MODELOS para as classes e funções relacionadas à cada modelo implementado na biblioteca.
AUXILIARES INTERNOS para as classes e funções usadas internamente.
Atualmente a biblioteca contém implementações do PyTorch, TensorFlow e JAX, pesos para modelos pré-treinados e scripts de uso e conversão de utilidades para os seguintes modelos:
Modelos atuais
- ALBERT (from Google Research and the Toyota Technological Institute at Chicago) released with the paper ALBERT: A Lite BERT for Self-supervised Learning of Language Representations, by Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.
- BART (from Facebook) released with the paper BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension by Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Ves Stoyanov and Luke Zettlemoyer.
- BARThez (from École polytechnique) released with the paper BARThez: a Skilled Pretrained French Sequence-to-Sequence Model by Moussa Kamal Eddine, Antoine J.-P. Tixier, Michalis Vazirgiannis.
- BARTpho (from VinAI Research) released with the paper BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese by Nguyen Luong Tran, Duong Minh Le and Dat Quoc Nguyen.
- BEiT (from Microsoft) released with the paper BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong, Furu Wei.
- BERT (from Google) released with the paper BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding by Jacob Devlin, Ming-Wei Chang, Kenton Lee and Kristina Toutanova.
- BERTweet (from VinAI Research) released with the paper BERTweet: A pre-trained language model for English Tweets by Dat Quoc Nguyen, Thanh Vu and Anh Tuan Nguyen.
- BERT For Sequence Generation (from Google) released with the paper Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
- BigBird-RoBERTa (from Google Research) released with the paper Big Bird: Transformers for Longer Sequences by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
- BigBird-Pegasus (from Google Research) released with the paper Big Bird: Transformers for Longer Sequences by Manzil Zaheer, Guru Guruganesh, Avinava Dubey, Joshua Ainslie, Chris Alberti, Santiago Ontanon, Philip Pham, Anirudh Ravula, Qifan Wang, Li Yang, Amr Ahmed.
- Blenderbot (from Facebook) released with the paper Recipes for building an open-domain chatbot by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
- BlenderbotSmall (from Facebook) released with the paper Recipes for building an open-domain chatbot by Stephen Roller, Emily Dinan, Naman Goyal, Da Ju, Mary Williamson, Yinhan Liu, Jing Xu, Myle Ott, Kurt Shuster, Eric M. Smith, Y-Lan Boureau, Jason Weston.
- BORT (from Alexa) released with the paper Optimal Subarchitecture Extraction For BERT by Adrian de Wynter and Daniel J. Perry.
- ByT5 (from Google Research) released with the paper ByT5: Towards a token-free future with pre-trained byte-to-byte models by Linting Xue, Aditya Barua, Noah Constant, Rami Al-Rfou, Sharan Narang, Mihir Kale, Adam Roberts, Colin Raffel.
- CamemBERT (from Inria/Facebook/Sorbonne) released with the paper CamemBERT: a Tasty French Language Model by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
- CANINE (from Google Research) released with the paper CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representation by Jonathan H. Clark, Dan Garrette, Iulia Turc, John Wieting.
- ConvNeXT (from Facebook AI) released with the paper A ConvNet for the 2020s by Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie.
- ConvNeXTV2 (from Facebook AI) released with the paper ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders by Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon, Saining Xie.
- CLIP (from OpenAI) released with the paper Learning Transferable Visual Models From Natural Language Supervision by Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, Ilya Sutskever.
- ConvBERT (from YituTech) released with the paper ConvBERT: Improving BERT with Span-based Dynamic Convolution by Zihang Jiang, Weihao Yu, Daquan Zhou, Yunpeng Chen, Jiashi Feng, Shuicheng Yan.
- CPM (from Tsinghua University) released with the paper CPM: A Large-scale Generative Chinese Pre-trained Language Model by Zhengyan Zhang, Xu Han, Hao Zhou, Pei Ke, Yuxian Gu, Deming Ye, Yujia Qin, Yusheng Su, Haozhe Ji, Jian Guan, Fanchao Qi, Xiaozhi Wang, Yanan Zheng, Guoyang Zeng, Huanqi Cao, Shengqi Chen, Daixuan Li, Zhenbo Sun, Zhiyuan Liu, Minlie Huang, Wentao Han, Jie Tang, Juanzi Li, Xiaoyan Zhu, Maosong Sun.
- CTRL (from Salesforce) released with the paper CTRL: A Conditional Transformer Language Model for Controllable Generation by Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong and Richard Socher.
- Data2Vec (from Facebook) released with the paper Data2Vec: A General Framework for Self-supervised Learning in Speech, Vision and Language by Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, Michael Auli.
- DeBERTa (from Microsoft) released with the paper DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
- DeBERTa-v2 (from Microsoft) released with the paper DeBERTa: Decoding-enhanced BERT with Disentangled Attention by Pengcheng He, Xiaodong Liu, Jianfeng Gao, Weizhu Chen.
- Decision Transformer (from Berkeley/Facebook/Google) released with the paper Decision Transformer: Reinforcement Learning via Sequence Modeling by Lili Chen, Kevin Lu, Aravind Rajeswaran, Kimin Lee, Aditya Grover, Michael Laskin, Pieter Abbeel, Aravind Srinivas, Igor Mordatch.
- DiT (from Microsoft Research) released with the paper DiT: Self-supervised Pre-training for Document Image Transformer by Junlong Li, Yiheng Xu, Tengchao Lv, Lei Cui, Cha Zhang, Furu Wei.
- DeiT (from Facebook) released with the paper Training data-efficient image transformers & distillation through attention by Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, Hervé Jégou.
- DETR (from Facebook) released with the paper End-to-End Object Detection with Transformers by Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko.
- DialoGPT (from Microsoft Research) released with the paper DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation by Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan.
- DistilBERT (from HuggingFace), released together with the paper DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter by Victor Sanh, Lysandre Debut and Thomas Wolf. The same method has been applied to compress GPT2 into DistilGPT2, RoBERTa into DistilRoBERTa, Multilingual BERT into DistilmBERT and a German version of DistilBERT.
- DPR (from Facebook) released with the paper Dense Passage Retrieval for Open-Domain Question Answering by Vladimir Karpukhin, Barlas Oğuz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
- DPT (from Intel Labs) released with the paper Vision Transformers for Dense Prediction by René Ranftl, Alexey Bochkovskiy, Vladlen Koltun.
- EfficientNet (from Google Research) released with the paper EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by Mingxing Tan and Quoc V. Le.
- EncoderDecoder (from Google Research) released with the paper Leveraging Pre-trained Checkpoints for Sequence Generation Tasks by Sascha Rothe, Shashi Narayan, Aliaksei Severyn.
- ELECTRA (from Google Research/Stanford University) released with the paper ELECTRA: Pre-training text encoders as discriminators rather than generators by Kevin Clark, Minh-Thang Luong, Quoc V. Le, Christopher D. Manning.
- FlauBERT (from CNRS) released with the paper FlauBERT: Unsupervised Language Model Pre-training for French by Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoît Crabbé, Laurent Besacier, Didier Schwab.
- FNet (from Google Research) released with the paper FNet: Mixing Tokens with Fourier Transforms by James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, Santiago Ontanon.
- Funnel Transformer (from CMU/Google Brain) released with the paper Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing by Zihang Dai, Guokun Lai, Yiming Yang, Quoc V. Le.
- GLPN (from KAIST) released with the paper Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepth by Doyeon Kim, Woonghyun Ga, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, Junmo Kim.
- GPT (from OpenAI) released with the paper Improving Language Understanding by Generative Pre-Training by Alec Radford, Karthik Narasimhan, Tim Salimans and Ilya Sutskever.
- GPT-2 (from OpenAI) released with the paper Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodeiand Ilya Sutskever.
- GPT-J (from EleutherAI) released in the repository kingoflolz/mesh-transformer-jax by Ben Wang and Aran Komatsuzaki.
- GPT Neo (from EleutherAI) released in the repository EleutherAI/gpt-neo by Sid Black, Stella Biderman, Leo Gao, Phil Wang and Connor Leahy.
- GPTSAN-japanese released in the repository tanreinama/GPTSAN by Toshiyuki Sakamoto(tanreinama).
- Hubert (from Facebook) released with the paper HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units by Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, Kushal Lakhotia, Ruslan Salakhutdinov, Abdelrahman Mohamed.
- I-BERT (from Berkeley) released with the paper I-BERT: Integer-only BERT Quantization by Sehoon Kim, Amir Gholami, Zhewei Yao, Michael W. Mahoney, Kurt Keutzer.
- ImageGPT (from OpenAI) released with the paper Generative Pretraining from Pixels by Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, Ilya Sutskever.
- LayoutLM (from Microsoft Research Asia) released with the paper LayoutLM: Pre-training of Text and Layout for Document Image Understanding by Yiheng Xu, Minghao Li, Lei Cui, Shaohan Huang, Furu Wei, Ming Zhou.
- LayoutLMv2 (from Microsoft Research Asia) released with the paper LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou.
- LayoutXLM (from Microsoft Research Asia) released with the paper LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding by Yiheng Xu, Tengchao Lv, Lei Cui, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Furu Wei.
- LED (from AllenAI) released with the paper Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.
- Longformer (from AllenAI) released with the paper Longformer: The Long-Document Transformer by Iz Beltagy, Matthew E. Peters, Arman Cohan.
- LUKE (from Studio Ousia) released with the paper LUKE: Deep Contextualized Entity Representations with Entity-aware Self-attention by Ikuya Yamada, Akari Asai, Hiroyuki Shindo, Hideaki Takeda, Yuji Matsumoto.
- mLUKE (from Studio Ousia) released with the paper mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models by Ryokan Ri, Ikuya Yamada, and Yoshimasa Tsuruoka.
- LXMERT (from UNC Chapel Hill) released with the paper LXMERT: Learning Cross-Modality Encoder Representations from Transformers for Open-Domain Question Answering by Hao Tan and Mohit Bansal.
- M2M100 (from Facebook) released with the paper Beyond English-Centric Multilingual Machine Translation by Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Edouard Grave, Michael Auli, Armand Joulin.
- MarianMT Machine translation models trained using OPUS data by Jörg Tiedemann. The Marian Framework is being developed by the Microsoft Translator Team.
- Mask2Former (from FAIR and UIUC) released with the paper Masked-attention Mask Transformer for Universal Image Segmentation by Bowen Cheng, Ishan Misra, Alexander G. Schwing, Alexander Kirillov, Rohit Girdhar.
- MaskFormer (from Meta and UIUC) released with the paper Per-Pixel Classification is Not All You Need for Semantic Segmentation by Bowen Cheng, Alexander G. Schwing, Alexander Kirillov.
- MBart (from Facebook) released with the paper Multilingual Denoising Pre-training for Neural Machine Translation by Yinhan Liu, Jiatao Gu, Naman Goyal, Xian Li, Sergey Edunov, Marjan Ghazvininejad, Mike Lewis, Luke Zettlemoyer.
- MBart-50 (from Facebook) released with the paper Multilingual Translation with Extensible Multilingual Pretraining and Finetuning by Yuqing Tang, Chau Tran, Xian Li, Peng-Jen Chen, Naman Goyal, Vishrav Chaudhary, Jiatao Gu, Angela Fan.
- Megatron-BERT (from NVIDIA) released with the paper Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
- Megatron-GPT2 (from NVIDIA) released with the paper Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism by Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper and Bryan Catanzaro.
- MPNet (from Microsoft Research) released with the paper MPNet: Masked and Permuted Pre-training for Language Understanding by Kaitao Song, Xu Tan, Tao Qin, Jianfeng Lu, Tie-Yan Liu.
- MT5 (from Google AI) released with the paper mT5: A massively multilingual pre-trained text-to-text transformer by Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel.
- Nyströmformer (from the University of Wisconsin - Madison) released with the paper Nyströmformer: A Nyström-Based Algorithm for Approximating Self-Attention by Yunyang Xiong, Zhanpeng Zeng, Rudrasis Chakraborty, Mingxing Tan, Glenn Fung, Yin Li, Vikas Singh.
- OneFormer (from SHI Labs) released with the paper OneFormer: One Transformer to Rule Universal Image Segmentation by Jitesh Jain, Jiachen Li, MangTik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi.
- Pegasus (from Google) released with the paper PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu.
- Perceiver IO (from Deepmind) released with the paper Perceiver IO: A General Architecture for Structured Inputs & Outputs by Andrew Jaegle, Sebastian Borgeaud, Jean-Baptiste Alayrac, Carl Doersch, Catalin Ionescu, David Ding, Skanda Koppula, Daniel Zoran, Andrew Brock, Evan Shelhamer, Olivier Hénaff, Matthew M. Botvinick, Andrew Zisserman, Oriol Vinyals, João Carreira.
- PhoBERT (from VinAI Research) released with the paper PhoBERT: Pre-trained language models for Vietnamese by Dat Quoc Nguyen and Anh Tuan Nguyen.
- PLBart (from UCLA NLP) released with the paper Unified Pre-training for Program Understanding and Generation by Wasi Uddin Ahmad, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang.
- PoolFormer (from Sea AI Labs) released with the paper MetaFormer is Actually What You Need for Vision by Yu, Weihao and Luo, Mi and Zhou, Pan and Si, Chenyang and Zhou, Yichen and Wang, Xinchao and Feng, Jiashi and Yan, Shuicheng.
- ProphetNet (from Microsoft Research) released with the paper ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
- QDQBert (from NVIDIA) released with the paper Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation by Hao Wu, Patrick Judd, Xiaojie Zhang, Mikhail Isaev and Paulius Micikevicius.
- REALM (from Google Research) released with the paper REALM: Retrieval-Augmented Language Model Pre-Training by Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat and Ming-Wei Chang.
- Reformer (from Google Research) released with the paper Reformer: The Efficient Transformer by Nikita Kitaev, Łukasz Kaiser, Anselm Levskaya.
- RemBERT (from Google Research) released with the paper Rethinking embedding coupling in pre-trained language models by Hyung Won Chung, Thibault Févry, Henry Tsai, M. Johnson, Sebastian Ruder.
- RegNet (from META Platforms) released with the paper Designing Network Design Space by Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár.
- ResNet (from Microsoft Research) released with the paper Deep Residual Learning for Image Recognition by Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun.
- RoBERTa (from Facebook), released together with the paper RoBERTa: A Robustly Optimized BERT Pretraining Approach by Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, Veselin Stoyanov.
- RoFormer (from ZhuiyiTechnology), released together with the paper RoFormer: Enhanced Transformer with Rotary Position Embedding by Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu.
- SegFormer (from NVIDIA) released with the paper SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers by Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M. Alvarez, Ping Luo.
- SEW (from ASAPP) released with the paper Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
- SEW-D (from ASAPP) released with the paper Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.
- SpeechToTextTransformer (from Facebook), released together with the paper fairseq S2T: Fast Speech-to-Text Modeling with fairseq by Changhan Wang, Yun Tang, Xutai Ma, Anne Wu, Dmytro Okhonko, Juan Pino.
- SpeechToTextTransformer2 (from Facebook), released together with the paper Large-Scale Self- and Semi-Supervised Learning for Speech Translation by Changhan Wang, Anne Wu, Juan Pino, Alexei Baevski, Michael Auli, Alexis Conneau.
- Splinter (from Tel Aviv University), released together with the paper Few-Shot Question Answering by Pretraining Span Selection by Ori Ram, Yuval Kirstain, Jonathan Berant, Amir Globerson, Omer Levy.
- SqueezeBert (from Berkeley) released with the paper SqueezeBERT: What can computer vision teach NLP about efficient neural networks? by Forrest N. Iandola, Albert E. Shaw, Ravi Krishna, and Kurt W. Keutzer.
- Swin Transformer (from Microsoft) released with the paper Swin Transformer: Hierarchical Vision Transformer using Shifted Windows by Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, Baining Guo.
- T5 (from Google AI) released with the paper Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
- T5v1.1 (from Google AI) released in the repository google-research/text-to-text-transfer-transformer by Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu.
- TAPAS (from Google AI) released with the paper TAPAS: Weakly Supervised Table Parsing via Pre-training by Jonathan Herzig, Paweł Krzysztof Nowak, Thomas Müller, Francesco Piccinno and Julian Martin Eisenschlos.
- TAPEX (from Microsoft Research) released with the paper TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou.
- Transformer-XL (from Google/CMU) released with the paper Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context by Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov.
- TrOCR (from Microsoft), released together with the paper TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.
- UniSpeech (from Microsoft Research) released with the paper UniSpeech: Unified Speech Representation Learning with Labeled and Unlabeled Data by Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang.
- UniSpeechSat (from Microsoft Research) released with the paper UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
- VAN (from Tsinghua University and Nankai University) released with the paper Visual Attention Network by Meng-Hao Guo, Cheng-Ze Lu, Zheng-Ning Liu, Ming-Ming Cheng, Shi-Min Hu.
- ViLT (from NAVER AI Lab/Kakao Enterprise/Kakao Brain) released with the paper ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision by Wonjae Kim, Bokyung Son, Ildoo Kim.
- Vision Transformer (ViT) (from Google AI) released with the paper An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
- ViTMAE (from Meta AI) released with the paper Masked Autoencoders Are Scalable Vision Learners by Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, Ross Girshick.
- VisualBERT (from UCLA NLP) released with the paper VisualBERT: A Simple and Performant Baseline for Vision and Language by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
- WavLM (from Microsoft Research) released with the paper WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
- Wav2Vec2 (from Facebook AI) released with the paper wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
- Wav2Vec2Phoneme (from Facebook AI) released with the paper Simple and Effective Zero-shot Cross-lingual Phoneme Recognition by Qiantong Xu, Alexei Baevski, Michael Auli.
- XGLM (From Facebook AI) released with the paper Few-shot Learning with Multilingual Language Models by Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, Tianlu Wang, Shuohui Chen, Daniel Simig, Myle Ott, Naman Goyal, Shruti Bhosale, Jingfei Du, Ramakanth Pasunuru, Sam Shleifer, Punit Singh Koura, Vishrav Chaudhary, Brian O’Horo, Jeff Wang, Luke Zettlemoyer, Zornitsa Kozareva, Mona Diab, Veselin Stoyanov, Xian Li.
- XLM (from Facebook) released together with the paper Cross-lingual Language Model Pretraining by Guillaume Lample and Alexis Conneau.
- XLM-ProphetNet (from Microsoft Research) released with the paper ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
- XLM-RoBERTa (from Facebook AI), released together with the paper Unsupervised Cross-lingual Representation Learning at Scale by Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
- XLM-RoBERTa-XL (from Facebook AI), released together with the paper Larger-Scale Transformers for Multilingual Masked Language Modeling by Naman Goyal, Jingfei Du, Myle Ott, Giri Anantharaman, Alexis Conneau.
- XLNet (from Google/CMU) released with the paper XLNet: Generalized Autoregressive Pretraining for Language Understanding by Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, Quoc V. Le.
- XLSR-Wav2Vec2 (from Facebook AI) released with the paper Unsupervised Cross-Lingual Representation Learning For Speech Recognition by Alexis Conneau, Alexei Baevski, Ronan Collobert, Abdelrahman Mohamed, Michael Auli.
- XLS-R (from Facebook AI) released with the paper XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale by Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli.
- YOSO (from the University of Wisconsin - Madison) released with the paper You Only Sample (Almost) Once: Linear Cost Self-Attention Via Bernoulli Sampling by Zhanpeng Zeng, Yunyang Xiong, Sathya N. Ravi, Shailesh Acharya, Glenn Fung, Vikas Singh.
Frameworks aceitos
A tabela abaixo representa a lista de suporte na biblioteca para cada um dos seguintes modelos, caso tenham um tokenizer do Python (chamado de “slow”), ou um tokenizer construído em cima da biblioteca 🤗 Tokenizers (chamado de “fast”). Além disso, são diferenciados pelo suporte em diferentes frameworks: JAX (por meio do Flax); PyTorch; e/ou Tensorflow.
| Model | Tokenizer slow | Tokenizer fast | PyTorch support | TensorFlow support | Flax Support |
|---|---|---|---|---|---|
| ALBERT | ✅ | ✅ | ✅ | ✅ | ✅ |
| BART | ✅ | ✅ | ✅ | ✅ | ✅ |
| BEiT | ❌ | ❌ | ✅ | ❌ | ✅ |
| BERT | ✅ | ✅ | ✅ | ✅ | ✅ |
| Bert Generation | ✅ | ❌ | ✅ | ❌ | ❌ |
| BigBird | ✅ | ✅ | ✅ | ❌ | ✅ |
| BigBirdPegasus | ❌ | ❌ | ✅ | ❌ | ❌ |
| Blenderbot | ✅ | ✅ | ✅ | ✅ | ✅ |
| BlenderbotSmall | ✅ | ✅ | ✅ | ✅ | ✅ |
| CamemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
| Canine | ✅ | ❌ | ✅ | ❌ | ❌ |
| CLIP | ✅ | ✅ | ✅ | ✅ | ✅ |
| ConvBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
| ConvNext | ❌ | ❌ | ✅ | ✅ | ❌ |
| CTRL | ✅ | ❌ | ✅ | ✅ | ❌ |
| Data2VecAudio | ❌ | ❌ | ✅ | ❌ | ❌ |
| Data2VecText | ❌ | ❌ | ✅ | ❌ | ❌ |
| Data2VecVision | ❌ | ❌ | ✅ | ❌ | ❌ |
| DeBERTa | ✅ | ✅ | ✅ | ✅ | ❌ |
| DeBERTa-v2 | ✅ | ✅ | ✅ | ✅ | ❌ |
| Decision Transformer | ❌ | ❌ | ✅ | ❌ | ❌ |
| DeiT | ❌ | ❌ | ✅ | ❌ | ❌ |
| DETR | ❌ | ❌ | ✅ | ❌ | ❌ |
| DistilBERT | ✅ | ✅ | ✅ | ✅ | ✅ |
| DPR | ✅ | ✅ | ✅ | ✅ | ❌ |
| DPT | ❌ | ❌ | ✅ | ❌ | ❌ |
| ELECTRA | ✅ | ✅ | ✅ | ✅ | ✅ |
| Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅ |
| FairSeq Machine-Translation | ✅ | ❌ | ✅ | ❌ | ❌ |
| FlauBERT | ✅ | ❌ | ✅ | ✅ | ❌ |
| FNet | ✅ | ✅ | ✅ | ❌ | ❌ |
| Funnel Transformer | ✅ | ✅ | ✅ | ✅ | ❌ |
| GLPN | ❌ | ❌ | ✅ | ❌ | ❌ |
| GPT Neo | ❌ | ❌ | ✅ | ❌ | ✅ |
| GPT-J | ❌ | ❌ | ✅ | ✅ | ✅ |
| Hubert | ❌ | ❌ | ✅ | ✅ | ❌ |
| I-BERT | ❌ | ❌ | ✅ | ❌ | ❌ |
| ImageGPT | ❌ | ❌ | ✅ | ❌ | ❌ |
| LayoutLM | ✅ | ✅ | ✅ | ✅ | ❌ |
| LayoutLMv2 | ✅ | ✅ | ✅ | ❌ | ❌ |
| LED | ✅ | ✅ | ✅ | ✅ | ❌ |
| Longformer | ✅ | ✅ | ✅ | ✅ | ❌ |
| LUKE | ✅ | ❌ | ✅ | ❌ | ❌ |
| LXMERT | ✅ | ✅ | ✅ | ✅ | ❌ |
| M2M100 | ✅ | ❌ | ✅ | ❌ | ❌ |
| Marian | ✅ | ❌ | ✅ | ✅ | ✅ |
| MaskFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
| mBART | ✅ | ✅ | ✅ | ✅ | ✅ |
| MegatronBert | ❌ | ❌ | ✅ | ❌ | ❌ |
| MobileBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
| MPNet | ✅ | ✅ | ✅ | ✅ | ❌ |
| mT5 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Nystromformer | ❌ | ❌ | ✅ | ❌ | ❌ |
| OpenAI GPT | ✅ | ✅ | ✅ | ✅ | ❌ |
| OpenAI GPT-2 | ✅ | ✅ | ✅ | ✅ | ✅ |
| Pegasus | ✅ | ✅ | ✅ | ✅ | ✅ |
| Perceiver | ✅ | ❌ | ✅ | ❌ | ❌ |
| PLBart | ✅ | ❌ | ✅ | ❌ | ❌ |
| PoolFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
| ProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
| QDQBert | ❌ | ❌ | ✅ | ❌ | ❌ |
| RAG | ✅ | ❌ | ✅ | ✅ | ❌ |
| Realm | ✅ | ✅ | ✅ | ❌ | ❌ |
| Reformer | ✅ | ✅ | ✅ | ❌ | ❌ |
| RegNet | ❌ | ❌ | ✅ | ✅ | ✅ |
| RemBERT | ✅ | ✅ | ✅ | ✅ | ❌ |
| ResNet | ❌ | ❌ | ✅ | ❌ | ✅ |
| RetriBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
| RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ |
| RoFormer | ✅ | ✅ | ✅ | ✅ | ✅ |
| SegFormer | ❌ | ❌ | ✅ | ❌ | ❌ |
| SEW | ❌ | ❌ | ✅ | ❌ | ❌ |
| SEW-D | ❌ | ❌ | ✅ | ❌ | ❌ |
| Speech Encoder decoder | ❌ | ❌ | ✅ | ❌ | ✅ |
| Speech2Text | ✅ | ❌ | ✅ | ✅ | ❌ |
| Speech2Text2 | ✅ | ❌ | ❌ | ❌ | ❌ |
| Splinter | ✅ | ✅ | ✅ | ❌ | ❌ |
| SqueezeBERT | ✅ | ✅ | ✅ | ❌ | ❌ |
| Swin | ❌ | ❌ | ✅ | ❌ | ❌ |
| T5 | ✅ | ✅ | ✅ | ✅ | ✅ |
| TAPAS | ✅ | ❌ | ✅ | ✅ | ❌ |
| TAPEX | ✅ | ✅ | ✅ | ✅ | ✅ |
| Transformer-XL | ✅ | ❌ | ✅ | ✅ | ❌ |
| TrOCR | ❌ | ❌ | ✅ | ❌ | ❌ |
| UniSpeech | ❌ | ❌ | ✅ | ❌ | ❌ |
| UniSpeechSat | ❌ | ❌ | ✅ | ❌ | ❌ |
| VAN | ❌ | ❌ | ✅ | ❌ | ❌ |
| ViLT | ❌ | ❌ | ✅ | ❌ | ❌ |
| Vision Encoder decoder | ❌ | ❌ | ✅ | ✅ | ✅ |
| VisionTextDualEncoder | ❌ | ❌ | ✅ | ❌ | ✅ |
| VisualBert | ❌ | ❌ | ✅ | ❌ | ❌ |
| ViT | ❌ | ❌ | ✅ | ✅ | ✅ |
| ViTMAE | ❌ | ❌ | ✅ | ✅ | ❌ |
| Wav2Vec2 | ✅ | ❌ | ✅ | ✅ | ✅ |
| WavLM | ❌ | ❌ | ✅ | ❌ | ❌ |
| XGLM | ✅ | ✅ | ✅ | ❌ | ✅ |
| XLM | ✅ | ❌ | ✅ | ✅ | ❌ |
| XLM-RoBERTa | ✅ | ✅ | ✅ | ✅ | ✅ |
| XLM-RoBERTa-XL | ❌ | ❌ | ✅ | ❌ | ❌ |
| XLMProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
| XLNet | ✅ | ✅ | ✅ | ✅ | ❌ |
| YOSO | ❌ | ❌ | ✅ | ❌ | ❌ |